Difference between revisions of "Sean G. Carver's Research Interests"

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* developing methods of SyID,
 
* developing methods of SyID,
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* applying methods of SysID to understand biological processes,
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* applying methods of SysID to understand biological processes, and
 
* advancing methods for teaching SysID (pedagogy).
 
* advancing methods for teaching SysID (pedagogy).
  
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By ''system identification'', I mean using statistics, derived from experimental data, to constrain models of the system.
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By ''system identification'', I mean ''using statistics, derived from experimental data, to constrain models of the system''.
  
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== Identifying the human balance, gait, and postural control system ==
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== System Identification of Cooperative Control ==
  
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By the fifth decade of life, most people perform significantly worse on clinical measures of balance.  For some, such poor performance creates little more than a minor nuisance.  But for many others, balance problems can be devastating, even life threatening.  For example, the most common reason for admittance to a nursing home is a recent fall.  The cost of falls in the elderly is often enormous -- including lengthy hospital stays and significant pain and suffering.
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== Control Theory Where Agent(s) Rely on Noisy Clocks ==
  
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One of the primary challenges to helping fall-prone patients is the heterogeneous nature of their population: no two patients are alike.  Many have sensory deficits, of various sorts.  Many have motor deficits.  Undoubtedly, there are also many stages of central processing that can go airy.  '''It would be useful to have a diagnostic tool to identify what aspects of the balance control system are failing.'''  That way, a therapist could focus the rehabilitation on the unique deficits of the patient, and could monitor the progress of the rehabilitation.  I envision a battery of tests and a systematic way of analyzing the data.  This process could be an application of system identification.
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== Pedagogy for System Identification ==
  
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== Identifying sensorimotor mechanisms at the cellular and network levels ==
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I am interested in finding effective methods for training students in the art of system identification.  One advantage of system identification over other laboratory techniques stems from the fact that one can collect data to be used in SysID from simulations.  In this way students can design and execute many interesting computer based experiments without a large investment in laboratory techniques and training.
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One of the things that fascinates me most studying nervous systems is that they can be studied on so many different scales.  While the emergent phenomena on the different levels differ so strikingly, they all fit together coherently.    I am interested in using neurophysiological data to identify sensorimotor systems at the cellular and network levels.  '''These efforts, aside from their independent scientific merit, fit into my broader clinical ambitions in that I expect them to lead to intuition that will help me generate hypotheses that can be tested at higher levels.'''
 
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== Testing and developing methods for identifying neural systems ==
 
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To date, inferring the structure and parameterization of models of neural systems is often done in ad hoc ways.  I am interested in creating tools for making such inferences in systematic ways.
 

Revision as of 19:24, 25 May 2013

Broadly, my interests involve system identification (SysID) applied to biological systems, especially:

  • developing methods of SyID,
  • applying methods of SysID to understand biological processes, and
  • advancing methods for teaching SysID (pedagogy).

By system identification, I mean using statistics, derived from experimental data, to constrain models of the system.

System Identification of Cooperative Control

Control Theory Where Agent(s) Rely on Noisy Clocks

Pedagogy for System Identification

I am interested in finding effective methods for training students in the art of system identification. One advantage of system identification over other laboratory techniques stems from the fact that one can collect data to be used in SysID from simulations. In this way students can design and execute many interesting computer based experiments without a large investment in laboratory techniques and training.

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